Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915265883668480 |
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| author | Yoo, Jinsun Lao, ChonLam Cao, Lianjie Lantz, Bob Yu, Minlan Krishna, Tushar Sharma, Puneet |
| author_facet | Yoo, Jinsun Lao, ChonLam Cao, Lianjie Lantz, Bob Yu, Minlan Krishna, Tushar Sharma, Puneet |
| contents | This paper lays the foundation for Genie, a testing framework that captures the impact of real hardware network behavior on ML workload performance, without requiring expensive GPUs. Genie uses CPU-initiated traffic over a hardware testbed to emulate GPU to GPU communication, and adapts the ASTRA-sim simulator to model interaction between the network and the ML workload. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Yoo, Jinsun Lao, ChonLam Cao, Lianjie Lantz, Bob Yu, Minlan Krishna, Tushar Sharma, Puneet Networking and Internet Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Systems and Control This paper lays the foundation for Genie, a testing framework that captures the impact of real hardware network behavior on ML workload performance, without requiring expensive GPUs. Genie uses CPU-initiated traffic over a hardware testbed to emulate GPU to GPU communication, and adapts the ASTRA-sim simulator to model interaction between the network and the ML workload. |
| title | Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning |
| topic | Networking and Internet Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Systems and Control |
| url | https://arxiv.org/abs/2504.20854 |